Comparing composite likelihood methods based on pairs for spatial Gaussian random fieldsM

2013/05/23 by Moreno Bevilacqua, Bevilacqua, Moreno, Carlo Gaetan +1 · 4 citations
Earth and Planetary Sciences · Environmental Science · Mathematics · #Hydrology and Drought Analysis #Precipitation Measurement and Analysis #Soil Geostatistics and Mapping #stat.ME

paper · pdf · doi:10.48550/arxiv.1305.5594

27 pages,6 figures

arxiv created 2013/05/24 · arxiv updated 2013/05/27

Abstract

In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when we deal with a Gaussian model. However maximum likelihood becomes impractical when the number of observations is very large. In this work we review some solutions and we contrast them in terms of loss of statistical efficiency and computational burden. Specifically we focus on three types of weighted composite likelihood functions based on pairs and we compare them with the method of covariance tapering. Asymptotics properties of the three estimation methods are derived. We illustrate the effectiveness of the methods through theoretical examples, simulation experiments and by analysing a data set on yearly total precipitation anomalies at weather stations in the United States.

Citations

• Maximum likelihood estimation of models for residual covariance in spatial regression

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